Training

CAMDU offers a range of training and workshop opportunities. Below you can find a summary of these sessions as well as access the associated training materials.

There will be no image analysis or computing training sessions from September 2025 - September 2026. Here are a list of resources for independent learning:

1. Watch an Introduction to Image Analysis:

Laura's lecture from 2024. Introduces image analysis and programming in Fiji.

2. Image Analysis Worksheets

Self paced worksheets for learning image analysis in Fiji

3. Introduction to Python with Napari:

The aim of this course is not to teach python, but to give you an idea of how it can be used for image analysis.
To get started:

Everyone: Download and unzip this folder (make sure you know where you've saved it) or you can clone the git repository
Beginner Python users: I recommend installing AnacondaLink opens in a new windowLink opens in a new window. After installing please follow the step by step instructions in AnacondaSetup.pdf.
Experienced Python users: Set up a virtual or conda environment with Python 3.9. Within the environment please install napariLink opens in a new windowLink opens in a new window (I use python -m pip install napari[all], check the instructions for your operating system if you have issues). I use jupyter notebooks but you can use your favourite IDE instead. After activating the environment, please check you can run the SetupCheck.py script (or SetupCheck.ipynb if using jupyter notebook) from the attached .zip folder.

You should get a Napari window that opens and looks like the picture SetupCheck.png.

Follow along with the training notebook typing and running the commands in the cells (use shift + click to run the cells)
To learn more about image analysis with python, take a look at Neubais bioimage analysis with python course

4. Attempt the CAMDU Challenge in Fiji or Python:

Consolidate your skills by trying them out on this challenge. Download the data set provided. The data set contains six microscopy images (.tif files), each with three channels: in the first, you have a a GFP-tagged protein of interest, which forms foci; on the second a cytoplasmic marker; and on the third a brightfield image of the cells.

You can try to write a code to do one or all of the following:

Identify the cell boundaries (channel two only, use only the most in focus plane)
Detect the foci (channel one only, use a maximum projection)
Count the foci in specific cells - combining the first two codes
Create a code to batch process all the files in the data set.